Papers with Code 아카이브

Medical Image Segmentation

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하위 태스크: 31
평가표: 49
보존된 평가 기록: 332

Medical Image Segmentation is a computer vision task that involves dividing an medical image into multiple segments, where each segment represents a different object or structure of interest in the image. The goal of medical image segmentation is to provide a precise and accurate representation of the objects of interest within the image, typically for the purpose of diagnosis, treatment planning, and quantitative analysis.

<span style="color:grey; opacity: 0.6">( Image credit: IVD-Net )</span>

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/JohnleeHIT/Brats2019

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Multi-step Cascaded Networks for Brain Tumor Segmentation

https://github.com/JunMa11/AbdomenCT-1K

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AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?

https://github.com/JunMa11/SegLoss

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Segmentation Loss Odyssey

https://github.com/JuzhengMiao/CPC-SAM

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Cross Prompting Consistency with Segment Anything Model for Semi-supervised Medical Image Segmentation

https://github.com/KCL-BMEIS/ScribbleDA

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Scribble-based Domain Adaptation via Co-segmentation

https://github.com/Kamnitsask/deepmedic

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A multi-path 2.5 dimensional convolutional neural network system for segmenting stroke lesions in brain MRI images

https://github.com/KeleiHe/BiGL

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Cross-Modality Brain Tumor Segmentation via Bidirectional Global-to-Local Unsupervised Domain Adaptation

https://github.com/King-HAW/ATLAS-R2-Docker-Submission

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MAPPING: Model Average with Post-processing for Stroke Lesion Segmentation

https://github.com/KrishnaswamyLab/DiffKillR

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DiffKillR: Killing and Recreating Diffeomorphisms for Cell Annotation in Dense Microscopy Images

https://github.com/KurtLabUW/ISLES2024

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How We Won the ISLES'24 Challenge by Preprocessing

https://github.com/L0SG/grouped-ssd-pytorch

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Robust End-to-End Focal Liver Lesion Detection using Unregistered Multiphase Computed Tomography Images

https://github.com/LIVIAETS/SizeLoss_WSS

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Constrained-CNN losses for weakly supervised segmentation

https://github.com/LIVIAETS/surface-loss

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Boundary loss for highly unbalanced segmentation

https://github.com/LanHEU/CTS

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CTS: A Consistency-Based Medical Image Segmentation Model

https://github.com/LiheYoung/UniMatch

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Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation

https://github.com/LiuTingWed/CriDiff

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CriDiff: Criss-cross Injection Diffusion Framework via Generative Pre-train for Prostate Segmentation

https://github.com/LongchaoDa/SegmentAsYouWish

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Segment as You Wish -- Free-Form Language-Based Segmentation for Medical Images

https://github.com/Luc4Gui/CAVM

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CAVM: Conditional Autoregressive Vision Model for Contrast-Enhanced Brain Tumor MRI Synthesis

https://github.com/LucasFidon/GeneralizedWassersteinDiceLoss

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Generalized Wasserstein Dice Score, Distributionally Robust Deep Learning, and Ranger for brain tumor segmentation: BraTS 2020 challenge

https://github.com/LucasFidon/HardnessWeightedSampler

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Distributionally Robust Deep Learning using Hardness Weighted Sampling

https://github.com/LucasFidon/fetal-brain-segmentation-partial-supervision-miccai21

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Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

https://github.com/LucasFidon/label-set-loss-functions

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Label-set Loss Functions for Partial Supervision: Application to Fetal Brain 3D MRI Parcellation

https://github.com/MASILab/ALARM

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Fully Automatic Liver Attenuation Estimation Combing CNN Segmentation and Morphological Operations

https://github.com/MASILab/SLANTbrainSeg

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Spatially Localized Atlas Network Tiles Enables 3D Whole Brain Segmentation from Limited Data

https://github.com/MECLabTUDA/Lifelong-nnUNet

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Continual Hippocampus Segmentation with Transformers

https://github.com/MECLabTUDA/M3D-NCA

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M3D-NCA: Robust 3D Segmentation with Built-in Quality Control

https://github.com/MIC-DKFZ/Longitudinal-Difference-Weighting

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Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

https://github.com/MIC-DKFZ/MedNeXt

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MedNeXt: Transformer-driven Scaling of ConvNets for Medical Image Segmentation

https://github.com/MIC-DKFZ/nnunet

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nnU-Net: Self-adapting Framework for U-Net-Based Medical Image Segmentation

https://github.com/MIMBCD-UI/prototype-multi-modality

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BreastScreening: On the Use of Multi-Modality in Medical Imaging Diagnosis

https://github.com/MIPT-Oulu/RobustCartilageSegmentation

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Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation

https://github.com/MJJdG/ULS23

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The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography

https://github.com/MarielleJansen/Liver-metastases-detection

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Liver segmentation and metastases detection in MR images using convolutional neural networks

https://github.com/MarielleJansen/Liver-segmentation

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Optimal input configuration of dynamic contrast enhanced MRI in convolutional neural networks for liver segmentation

https://github.com/McGregorWwww/UDTransNet

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Narrowing the semantic gaps in U-Net with learnable skip connections: The case of medical image segmentation

https://github.com/MengzhangLI/ASNG

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Automatic Data Augmentation for 3D Medical Image Segmentation

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.